{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import sys \n",
    "from os import getcwd, path\n",
    "sys.path.append(path.dirname(getcwd()))\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [],
   "source": [
    "from utils import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "autoscroll": "json-false",
    "collapsed": false,
    "ein.tags": [
     "worksheet-0"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'dataframe_hash': -2899676230513618006,\n",
      " 'provenance_file_summary': {u'cohorts': u'0.4.0+3.gda968fb',\n",
      "                             u'isovar': u'0.0.6',\n",
      "                             u'mhctools': u'0.3.0',\n",
      "                             u'numpy': u'1.11.1',\n",
      "                             u'pandas': u'0.18.1',\n",
      "                             u'pyensembl': u'1.0.3',\n",
      "                             u'scipy': u'0.18.1',\n",
      "                             u'topiary': u'0.1.0',\n",
      "                             u'varcode': u'0.5.10'}}\n"
     ]
    }
   ],
   "source": [
    "cohort = data.init_cohort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cohorts.functions import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from utils.paper import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.stats import spearmanr, pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with ensembl_coverage: 25 to 25 rows\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{missense_vs_pdl1_plot}}}\n",
      "{{{missense_vs_pdl1_spearmanr:n=25, Spearman rho=0.14 p=0.51}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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8PHx9fQkICCixPDg4mHPnzjl9fyIiUr4yC+Pw4cPUq1cPAMMwGDp0KEOHDi2xzoMPPsiD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\nREQs+f+oyAC9P/zqYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd730f46950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spearmanr_hyper_label_printer(cohort.plot_correlation({\"PD-L1 IC\": lambda row: int(row[\"PD-L1\"].split(\"IC\")[1]),\n",
    "                                    \"# Missense SNVs / MB\": missense_snv_count},\n",
    "                                    plot_type=\"boxplot\",\n",
    "                                    x_col=\"PD-L1 IC\",\n",
    "                                    stat_func=spearmanr), label=\"missense_vs_pdl1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with tcr_peripheral_a: 25 to 25 rows\n",
      "inner join with tcr_peripheral_a: 25 to 25 rows\n",
      "{'dataframe_hash': -6373961267847645653,\n",
      " 'provenance_file_summary': {u'cohorts': u'0.4.0+3.gda968fb',\n",
      "                             u'isovar': u'0.0.6',\n",
      "                             u'mhctools': u'0.3.0',\n",
      "                             u'numpy': u'1.11.1',\n",
      "                             u'pandas': u'0.18.1',\n",
      "                             u'pyensembl': u'1.0.3',\n",
      "                             u'scipy': u'0.18.1',\n",
      "                             u'topiary': u'0.1.0',\n",
      "                             u'varcode': u'0.5.10'}}\n"
     ]
    }
   ],
   "source": [
    "cohort = data.init_cohort(join_with=\"tcr_peripheral_a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with tcr_peripheral_a: 25 to 25 rows\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner join with ensembl_coverage: 25 to 25 rows\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{{{missense_vs_tcr_plot}}}\n",
      "{{{missense_vs_tcr_pearsonr:n=25, Pearson r=0.0017 p=0.99}}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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vvqv4NomIqHRkhYqlpSXUajWePHmC6OhoCIKAunXrwtraWnpcad988w0sLS3xn//8BxUr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\nChERKYahQkREimGoEBGRYhgqRESkmP8H+d/uSswtGewAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd72e6e2bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pearsonr_hyper_label_printer(cohort.plot_correlation({\"Pre-treatment TCR Clonality\": lambda row: \n",
    "                                                 row[\"Clonality\"],\n",
    "                                    \"# Missense SNVs / MB\": missense_snv_count},\n",
    "                                    plot_type=\"jointplot\",\n",
    "                                    x_col=\"Pre-treatment TCR Clonality\",\n",
    "                                    show_stat_func=False,\n",
    "                                    stat_func=pearsonr), label=\"missense_vs_tcr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  },
  "name": "PD-L1.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
